Contributions To The Methodology Of Electrocardiographic Imaging (ECGI) And Application Of ECGI To Study Mechanisms Of Atrial Arrhythmia, Post Myocardial Infarction Electrophysiological Substrate, And Ventricular Tachycardia In Patients

ABSTRACT OF THE DISSERTATION Contributions to the Methodology of Electrocardiographic Imaging: ECGI) and Application of ECGI to Study Mechanisms of Atrial Arrhythmia, Post Myocardial Infarction Electrophysiological Substrate, and Ventricular Tachycardia in Patients by Yong Wang Doctor of Philosophy in Biomedical Engineering Washington University in St. Louis, 2009 Professor Yoram Rudy, Chair Electrocardiographic Imaging: ECGI) is a noninvasive imaging modality for cardiac electrophysiology and arrhythmia. ECGI reconstructs epicardial potentials, electrograms and isochrones from body-surface electrocardiograms combined with heart-torso geometry from computed tomography: CT). The application of a new meshless method, the Method of Fundamental Solutions: MFS) is introduced to ECGI with the following major advantages: 1. Elimination of meshing and manual mesh optimization processes, thereby enhancing automation and speeding the ECGI procedure. 2. Elimination of mesh-induced artifacts. 3. Simpler implementation. These properties of MFS enhance the practical application of ECGI as a clinical diagnostic tool. The current ECGI mode of operation is offline with generation of epicardial potential maps delayed to data acquisition. A real time ECGI procedure is proposed, by which the epicardial potentials can be reconstructed while the body surface potential data are acquired: \u3c 1msec/frame) during a clinical procedure. This development enables real-time monitoring, diagnosis, and interactive guidance of intervention for arrhythmia therapy. ECGI is applied to map noninvasively the electrophysiological substrate in eight post-MI patients during sinus rhythm: SR). Contrast-enhanced MRI: ceMRI) is conducted to determine anatomical scar. ECGI imaged regions of electrical scar corresponded closely in location, extent, and morphology to the anatomical scars. In three patients, late diastolic potentials are imaged in the scar epicardial border zone during SR. Scar-related ventricular tachycardia: VT) in two patients are imaged, showing the VT activation sequence in relation to the abnormal electrophysiological substrate. ECGI imaging the substrate in a beat-by-beat fashion could potentially help in noninvasive risk stratification for post-MI arrhythmias and facilitate substrate-based catheter ablation of these arrhythmias. ECGI is applied to eleven consecutive patients referred for VT catheter ablation procedure. ECGI is performed either before: 8 patients) or during: 3 patients) the ablation procedure. Blinded ECGI and invasive electrophysiology: EP) study results are compared. Over a wide range of VT types and locations, ECGI results are consistent with EP data regarding localization of the arrhythmia origin: including myocardial depth) and mechanism: focal, reentrant, fascicular). ECGI also provides mechanistic electrophysiological insights, relating arrhythmia patterns to the myocardial substrate. The study shows ECGI has unique potential clinical advantages, especially for hemodynamically intolerant VT or VT that is difficult to induce. Because it provides local cardiac information, ECGI may aid in better understanding of mechanisms of ventricular arrhythmia. Further prospective trials of ECGI with clinical endpoints are warranted. Many mechanisms for the initiation and perpetuation of atrial fibrillation: AF) have been demonstrated over the last several decades. The tools to study these mechanisms in humans have limitations, the most common being invasiveness of a mapping procedure. In this paper, we present simultaneous noninvasive biatrial epicardial activation sequences of AF in humans, obtained using the Electrocardiographic Imaging: ECGI) system, and analyzed in terms of mechanisms and complexity of activation patterns. We performed ECGI in 36 patients with a diagnosis of AF. To determine ECGI atrial accuracy, atrial pacing from different sites was performed in six patients: 37 pacing events), and ECGI was compared to registered CARTO images. Then, ECGI was performed on all 36 patients during AF and ECGI epicardial maps were analyzed for mechanisms and complexity. ECGI noninvasively imaged the low-amplitude signals of AF in a wide range of patients: 97% procedural success). The spatial accuracy in determining initiation sites as simulated by atrial pacing was ~ 6mm. ECGI imaged many activation patterns of AF, most commonly multiple wavelets: 92%), with pulmonary vein: 69%) and non-pulmonary vein: 62%) trigger sites. Rotor activity was seen rarely: 15%). AF complexity increased with longer clinical history of AF, though the degree of complexity of nonparoxysmal AF varied and overlapped. ECGI offers a way to identify unique epicardial activation patterns of AF in a patient-specific manner. The results are consistent with contemporary animal models of AF mechanisms and highlight the coexistence of a variety of mechanisms among patients

Validation and Opportunities of Electrocardiographic Imaging: From Technical chievements to Clinical Applications

[EN] Electrocardiographic imaging (ECGI) reconstructs the electrical activity of the heart from a dense array of body-surface electrocardiograms and a patient-specific heart-torso geometry. Depending on how it is formulated, ECGI allows the reconstruction of the activation and recovery sequence of the heart, the origin of premature beats or tachycardia, the anchors/hotspots of re-entrant arrhythmias and other electrophysiological quantities of interest. Importantly, these quantities are directly and non-invasively reconstructed in a digitized model of the patient's three-dimensional heart, which has led to clinical interest in ECGI's ability to personalize diagnosis and guide therapy. Despite considerable development over the last decades, validation of ECGI is challenging. Firstly, results depend considerably on implementation choices, which are necessary to deal with ECGI's ill-posed character. Secondly, it is challenging to obtain (invasive) ground truth data of high quality. In this review, we discuss the current status of ECGI validation as well as the major challenges remaining for complete adoption of ECGI in clinical practice. Specifically, showing clinical benefit is essential for the adoption of ECGI. Such benefit may lie in patient outcome improvement, workflow improvement, or cost reduction. Future studies should focus on these aspects to achieve broad adoption of ECGI, but only after the technical challenges have been solved for that specific application/pathology. We propose 'best' practices for technical validation and highlight collaborative efforts recently organized in this field. Continued interaction between engineers, basic scientists, and physicians remains essential to find a hybrid between technical achievements, pathological mechanisms insights, and clinical benefit, to evolve this powerful technique toward a useful role in clinical practice.This study received financial support from the Hein Wellens Fonds, the Cardiovascular Research and Training Institute (CVRTI), the Nora Eccles Treadwell Foundation, the National Institute of General Medical Sciences of the National Institutes of Health (P41GM103545), the National Institutes of Health (NIH HL080093), the French government as part of the Investments of the Future program managed by the National Research Agency (ANR-10-IAHU-04), from the VEGA Grant Agency in Slovakia (2/0071/16), from the Slovak Research and Development Agency (APVV-14-0875), the Fondo Europeo de Desarrollo Regional (FEDER), the Instituto de Salud Carlos III (PI17/01106) and from Conselleria d'Educacio, Investigacio, Cultura i Esport de la Generalitat Valenciana (AICO/2018/267) and NIH grant (HL125998) and National Science Foundation (ACI-1350374).Cluitmans, M.; Brooks, D.; Macleod, RS.; Dossel, O.; Guillem Sánchez, MS.; Van Dam, P.; Svehlikova, J.... (2018). Validation and Opportunities of Electrocardiographic Imaging: From Technical chievements to Clinical Applications. Frontiers in Physiology. 9. https://doi.org/10.3389/fphys.2018.01305S9Andrews, C. M., Srinivasan, N. T., Rosmini, S., Bulluck, H., Orini, M., Jenkins, S., … Rudy, Y. (2017). Electrical and Structural Substrate of Arrhythmogenic Right Ventricular Cardiomyopathy Determined Using Noninvasive Electrocardiographic Imaging and Late Gadolinium Magnetic Resonance Imaging. Circulation: Arrhythmia and Electrophysiology, 10(7). doi:10.1161/circep.116.005105Aras, K., Good, W., Tate, J., Burton, B., Brooks, D., Coll-Font, J., … MacLeod, R. (2015). Experimental Data and Geometric Analysis Repository—EDGAR. Journal of Electrocardiology, 48(6), 975-981. doi:10.1016/j.jelectrocard.2015.08.008Austen, W., Edwards, J., Frye, R., Gensini, G., Gott, V., Griffith, L., … Roe, B. (1975). A reporting system on patients evaluated for coronary artery disease. Report of the Ad Hoc Committee for Grading of Coronary Artery Disease, Council on Cardiovascular Surgery, American Heart Association. Circulation, 51(4), 5-40. doi:10.1161/01.cir.51.4.5Bayley, R. H., & Berry, P. M. (1962). The electrical field produced by the eccentric current dipole in the nonhomogeneous conductor. American Heart Journal, 63(6), 808-820. doi:10.1016/0002-8703(62)90065-0Bear, L. R., Huntjens, P. R., Walton, R. D., Bernus, O., Coronel, R., & Dubois, R. (2018). Cardiac electrical dyssynchrony is accurately detected by noninvasive electrocardiographic imaging. Heart Rhythm, 15(7), 1058-1069. doi:10.1016/j.hrthm.2018.02.024Bear, L. R., LeGrice, I. J., Sands, G. B., Lever, N. A., Loiselle, D. S., Paterson, D. J., … Smaill, B. H. (2018). How Accurate Is Inverse Electrocardiographic Mapping? Circulation: Arrhythmia and Electrophysiology, 11(5). doi:10.1161/circep.117.006108Berger, T., Fischer, G., Pfeifer, B., Modre, R., Hanser, F., Trieb, T., … Hintringer, F. (2006). Single-Beat Noninvasive Imaging of Cardiac Electrophysiology of Ventricular Pre-Excitation. Journal of the American College of Cardiology, 48(10), 2045-2052. doi:10.1016/j.jacc.2006.08.019Berger, T., Pfeifer, B., Hanser, F. F., Hintringer, F., Fischer, G., Netzer, M., … Seger, M. (2011). Single-Beat Noninvasive Imaging of Ventricular Endocardial and Epicardial Activation in Patients Undergoing CRT. PLoS ONE, 6(1), e16255. doi:10.1371/journal.pone.0016255Dubois, R., Pashaei, A., Duchateau, J., & Vigmond, E. (2016). Evaluation of Combined Noninvasive Electrocardiographic Imaging and Phase Mapping approach for Atrial Fibrillation: A Simulation Study. 2016 Computing in Cardiology Conference (CinC). doi:10.22489/cinc.2016.037-540Duchateau, J., Potse, M., & Dubois, R. (2017). Spatially Coherent Activation Maps for Electrocardiographic Imaging. IEEE Transactions on Biomedical Engineering, 64(5), 1149-1156. doi:10.1109/tbme.2016.2593003Erem, B., Brooks, D. H., van Dam, P. M., Stinstra, J. G., & MacLeod, R. S. (2011). Spatiotemporal estimation of activation times of fractionated ECGs on complex heart surfaces. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. doi:10.1109/iembs.2011.6091455Erem, B., van Dam, P. M., & Brooks, D. H. (2014). Identifying Model Inaccuracies and Solution Uncertainties in Noninvasive Activation-Based Imaging of Cardiac Excitation Using Convex Relaxation. IEEE Transactions on Medical Imaging, 33(4), 902-912. doi:10.1109/tmi.2014.2297952Erkapic, D., & Neumann, T. (2015). Ablation of Premature Ventricular Complexes Exclusively Guided by Three-Dimensional Noninvasive Mapping. Cardiac Electrophysiology Clinics, 7(1), 109-115. doi:10.1016/j.ccep.2014.11.010Everett, T. H., Lai-Chow Kok, Vaughn, R. H., Moorman, R., & Haines, D. E. (2001). Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy. IEEE Transactions on Biomedical Engineering, 48(9), 969-978. doi:10.1109/10.942586Faes, L., & Ravelli, F. (2007). A morphology-based approach to the evaluation of atrial fibrillation organization. IEEE Engineering in Medicine and Biology Magazine, 26(4), 59-67. doi:10.1109/memb.2007.384097Fitzpatrick, A. P., Gonzales, R. P., Lesh, M. D., odin, G. W., Lee, R. J., & Scheinman, M. M. (1994). New algorithm for the localization of accessory atrioventricular connections using a baseline electrocardiogram. Journal of the American College of Cardiology, 23(1), 107-116. doi:10.1016/0735-1097(94)90508-8Geselowitz, D. B. (1989). On the theory of the electrocardiogram. Proceedings of the IEEE, 77(6), 857-876. doi:10.1109/5.29327Geselowitz, D. B. (1992). Description of cardiac sources in anisotropic cardiac muscle. Journal of Electrocardiology, 25, 65-67. doi:10.1016/0022-0736(92)90063-6Ghanem, R. N., Jia, P., Ramanathan, C., Ryu, K., Markowitz, A., & Rudy, Y. (2005). Noninvasive Electrocardiographic Imaging (ECGI): Comparison to intraoperative mapping in patients. Heart Rhythm, 2(4), 339-354. doi:10.1016/j.hrthm.2004.12.022Ghosh, S., Rhee, E. K., Avari, J. N., Woodard, P. K., & Rudy, Y. (2008). Cardiac Memory in Patients With Wolff-Parkinson-White Syndrome. Circulation, 118(9), 907-915. doi:10.1161/circulationaha.108.781658Ghosh, S., Silva, J. N. A., Canham, R. M., Bowman, T. M., Zhang, J., Rhee, E. K., … Rudy, Y. (2011). Electrophysiologic substrate and intraventricular left ventricular dyssynchrony in nonischemic heart failure patients undergoing cardiac resynchronization therapy. Heart Rhythm, 8(5), 692-699. doi:10.1016/j.hrthm.2011.01.017Grace, A., Verma, A., & Willems, S. (2017). Dipole Density Mapping of Atrial Fibrillation. European Heart Journal, 38(1), 5-9. doi:10.1093/eurheartj/ehw585Dorset, D. L. (1996). Electron crystallography. Acta Crystallographica Section B Structural Science, 52(5), 753-769. doi:10.1107/s0108768196005599Haissaguerre, M., Hocini, M., Denis, A., Shah, A. J., Komatsu, Y., Yamashita, S., … Dubois, R. (2014). Driver Domains in Persistent Atrial Fibrillation. Circulation, 130(7), 530-538. doi:10.1161/circulationaha.113.005421HAISSAGUERRE, M., HOCINI, M., SHAH, A. J., DERVAL, N., SACHER, F., JAIS, P., & DUBOIS, R. (2013). Noninvasive Panoramic Mapping of Human Atrial Fibrillation Mechanisms: A Feasibility Report. Journal of Cardiovascular Electrophysiology, 24(6), 711-717. doi:10.1111/jce.12075Han, C., Pogwizd, S. M., Killingsworth, C. R., & He, B. (2011). Noninvasive imaging of three-dimensional cardiac activation sequence during pacing and ventricular tachycardia. Heart Rhythm, 8(8), 1266-1272. doi:10.1016/j.hrthm.2011.03.014Bin He, Guanglin Li, & Xin Zhang. (2003). Noninvasive imaging of cardiac transmembrane potentials within three-dimensional myocardium by means of a realistic geometry anisotropic heart model. IEEE Transactions on Biomedical Engineering, 50(10), 1190-1202. doi:10.1109/tbme.2003.817637Bin He, & Dongsheng Wu. (2001). Imaging and visualization of 3-D cardiac electric activity. IEEE Transactions on Information Technology in Biomedicine, 5(3), 181-186. doi:10.1109/4233.945288Horáček, B. M., Sapp, J. L., Penney, C. J., Warren, J. W., & Wang, J. J. (2011). Comparison of epicardial potential maps derived from the 12-lead electrocardiograms with scintigraphic images during controlled myocardial ischemia. Journal of Electrocardiology, 44(6), 707-712. doi:10.1016/j.jelectrocard.2011.08.009Horáček, B. M., Wang, L., Dawoud, F., Xu, J., & Sapp, J. L. (2015). Noninvasive electrocardiographic imaging of chronic myocardial infarct scar. Journal of Electrocardiology, 48(6), 952-958. doi:10.1016/j.jelectrocard.2015.08.035Jamil-Copley, S., Vergara, P., Carbucicchio, C., Linton, N., Koa-Wing, M., Luther, V., … Kanagaratnam, P. (2015). Application of Ripple Mapping to Visualize Slow Conduction Channels Within the Infarct-Related Left Ventricular Scar. Circulation: Arrhythmia and Electrophysiology, 8(1), 76-86. doi:10.1161/circep.114.001827Janssen, A. M., Potyagaylo, D., Dössel, O., & Oostendorp, T. F. (2017). Assessment of the equivalent dipole layer source model in the reconstruction of cardiac activation times on the basis of BSPMs produced by an anisotropic model of the heart. Medical & Biological Engineering & Computing, 56(6), 1013-1025. doi:10.1007/s11517-017-1715-xKnecht, S., Sohal, M., Deisenhofer, I., Albenque, J.-P., Arentz, T., Neumann, T., … Rostock, T. (2017). Multicentre evaluation of non-invasive biatrial mapping for persistent atrial fibrillation ablation: the AFACART study. EP Europace, 19(8), 1302-1309. doi:10.1093/europace/euw168Kuck, K.-H., Schaumann, A., Eckardt, L., Willems, S., Ventura, R., Delacrétaz, E., … Hansen, P. S. (2010). Catheter ablation of stable ventricular tachycardia before defibrillator implantation in patients with coronary heart disease (VTACH): a multicentre randomised controlled trial. The Lancet, 375(9708), 31-40. doi:10.1016/s0140-6736(09)61755-4Identification of Rotors during Human Atrial Fibrillation Using Contact Mapping and Phase Singularity Detection: Technical Considerations. (2017). IEEE Transactions on Biomedical Engineering, 64(2), 310-318. doi:10.1109/tbme.2016.2554660Leong, K. M. W., Ng, F. S., Yao, C., Roney, C., Taraborrelli, P., Linton, N. W. F., … Varnava, A. M. (2017). ST-Elevation Magnitude Correlates With Right Ventricular Outflow Tract Conduction Delay in Type I Brugada ECG. Circulation: Arrhythmia and Electrophysiology, 10(10). doi:10.1161/circep.117.005107Chenguang Liu, Eggen, M. D., Swingen, C. M., Iaizzo, P. A., & Bin He. (2012). Noninvasive Mapping of Transmural Potentials During Activation in Swine Hearts From Body Surface Electrocardiograms. IEEE Transactions on Medical Imaging, 31(9), 1777-1785. doi:10.1109/tmi.2012.2202914MacLeod, R. S., Ni, Q., Punske, B., Ershler, P. R., Yilmaz, B., & Taccardi, B. (2000). Effects of heart position on the body-surface electrocardiogram. Journal of Electrocardiology, 33, 229-237. doi:10.1054/jelc.2000.20357Metzner, A., Wissner, E., Tsyganov, A., Kalinin, V., Schlüter, M., Lemes, C., … Kuck, K.-H. (2017). Noninvasive phase mapping of persistent atrial fibrillation in humans: Comparison with invasive catheter mapping. Annals of Noninvasive Electrocardiology, 23(4), e12527. doi:10.1111/anec.12527Modre, R., Tilg, B., Fischer, G., Hanser, F., Messnarz, B., Seger, M., … Roithinger, F. X. (2003). Atrial Noninvasive Activation Mapping of Paced Rhythm Data. Journal of Cardiovascular Electrophysiology, 14(7), 712-719. doi:10.1046/j.1540-8167.2003.02558.xNarayan, S. M., Krummen, D. E., Shivkumar, K., Clopton, P., Rappel, W.-J., & Miller, J. M. (2012). Treatment of Atrial Fibrillation by the Ablation of Localized Sources. Journal of the American College of Cardiology, 60(7), 628-636. doi:10.1016/j.jacc.2012.05.022NG, J., KADISH, A. H., & GOLDBERGER, J. J. (2007). Technical Considerations for Dominant Frequency Analysis. Journal of Cardiovascular Electrophysiology, 18(7), 757-764. doi:10.1111/j.1540-8167.2007.00810.xOosterhoff, P., Meijborg, V. M. F., van Dam, P. M., van Dessel, P. F. H. M., Belterman, C. N. W., Streekstra, G. J., … Oostendorp, T. F. (2016). Experimental Validation of Noninvasive Epicardial and Endocardial Activation Imaging. Circulation: Arrhythmia and Electrophysiology, 9(8). doi:10.1161/circep.116.004104Oster, H. S., Taccardi, B., Lux, R. L., Ershler, P. R., & Rudy, Y. (1997). Noninvasive Electrocardiographic Imaging. Circulation, 96(3), 1012-1024. doi:10.1161/01.cir.96.3.1012Oster, H. S., Taccardi, B., Lux, R. L., Ershler, P. R., & Rudy, Y. (1998). Electrocardiographic Imaging. Circulation, 97(15), 1496-1507. doi:10.1161/01.cir.97.15.1496PEDRÓN-TORRECILLA, J., RODRIGO, M., CLIMENT, A. M., LIBEROS, A., PÉREZ-DAVID, E., BERMEJO, J., … GUILLEM, M. S. (2016). Noninvasive Estimation of Epicardial Dominant High-Frequency Regions During Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 27(4), 435-442. doi:10.1111/jce.12931Ploux, S., Lumens, J., Whinnett, Z., Montaudon, M., Strom, M., Ramanathan, C., … Bordachar, P. (2013). Noninvasive Electrocardiographic Mapping to Improve Patient Selection for Cardiac Resynchronization Therapy. Journal of the American College of Cardiology, 61(24), 2435-2443. doi:10.1016/j.jacc.2013.01.093Potyagaylo, D., Segel, M., Schulze, W. H. W., & Dössel, O. (2013). Noninvasive Localization of Ectopic Foci: A New Optimization Approach for Simultaneous Reconstruction of Transmembrane Voltages and Epicardial Potentials. Lecture Notes in Computer Science, 166-173. doi:10.1007/978-3-642-38899-6_20Punshchykova, O., Švehlíková, J., Tyšler, M., Grünes, R., Sedova, K., Osmančík, P., … Kneppo, P. (2016). Influence of Torso Model Complexity on the Noninvasive Localization of Ectopic Ventricular Activity. Measurement Science Review, 16(2), 96-102. doi:10.1515/msr-2016-0013RAMANATHAN, C., & RUDY, Y. (2001). Electrocardiographic Imaging: II. Effect of Torso Inhomogeneities on Noninvasive Reconstruction of Epicardial Potentials, Electrograms, and Isochrones. Journal of Cardiovascular Electrophysiology, 12(2), 241-252. doi:10.1046/j.1540-8167.2001.00241.xReddy, V. Y., Reynolds, M. R., Neuzil, P., Richardson, A. W., Taborsky, M., Jongnarangsin, K., … Josephson, M. E. (2007). Prophylactic Catheter Ablation for the Prevention of Defibrillator Therapy. New England Journal of Medicine, 357(26), 2657-2665. doi:10.1056/nejmoa065457Rodrigo, M., Climent, A. M., Liberos, A., Fernández-Avilés, F., Berenfeld, O., Atienza, F., & Guillem, M. S. (2017). Technical Considerations on Phase Mapping for Identification of Atrial Reentrant Activity in Direct- and Inverse-Computed Electrograms. Circulation: Arrhythmia and Electrophysiology, 10(9). doi:10.1161/circep.117.005008ROTEN, L., PEDERSEN, M., PASCALE, P., SHAH, A., ELIAUTOU, S., SCHERR, D., … HAÏSSAGUERRE, M. (2012). Noninvasive Electrocardiographic Mapping for Prediction of Tachycardia Mechanism and Origin of Atrial Tachycardia Following Bilateral Pulmonary Transplantation. Journal of Cardiovascular Electrophysiology, 23(5), 553-555. doi:10.1111/j.1540-8167.2011.02250.xRudy, Y. (2013). Noninvasive Electrocardiographic Imaging of Arrhythmogenic Substrates in Humans. Circulation Research, 112(5), 863-874. doi:10.1161/circresaha.112.279315Ghosh, S., Avari, J. N., Rhee, E. K., Woodard, P. K., & Rudy, Y. (2008). Noninvasive electrocardiographic imaging (ECGI) of epicardial activation before and after catheter ablation of the accessory pathway in a patient with Ebstein anomaly. Heart Rhythm, 5(6), 857-860. doi:10.1016/j.hrthm.2008.03.011Rudy, Y., Plonsey, R., & Liebman, J. (1979). The effects of variations in conductivity and geometrical parameters on the electrocardiogram, using an eccentric spheres model. Circulation Research, 44(1), 104-111. doi:10.1161/01.res.44.1.104SALINET, J. L., TUAN, J. H., SANDILANDS, A. J., STAFFORD, P. J., SCHLINDWEIN, F. S., & NG, G. A. (2013). Distinctive Patterns of Dominant Frequency Trajectory Behavior in Drug-Refractory Persistent Atrial Fibrillation: Preliminary Characterization of Spatiotemporal Instability. Journal of Cardiovascular Electrophysiology, 25(4), 371-379. doi:10.1111/jce.12331Dalu, Y. (1978). Relating the multipole moments of the heart to activated parts of the epicardium and endocardium. Annals of Biomedical Engineering, 6(4), 492-505. doi:10.1007/bf02584552Sánchez, C., Bueno-Orovio, A., Pueyo, E., & Rodríguez, B. (2017). Atrial Fibrillation Dynamics and Ionic Block Effects in Six Heterogeneous Human 3D Virtual Atria with Distinct Repolarization Dynamics. Frontiers in Bioengineering and Biotechnology, 5. doi:10.3389/fbioe.2017.00029Sanders, P., Berenfeld, O., Hocini, M., Jaïs, P., Vaidyanathan, R., Hsu, L.-F., … Haïssaguerre, M. (2005). Spectral Analysis Identifies Sites of High-Frequency Activity Maintaining Atrial Fibrillation in Humans. Circulation, 112(6), 789-797. doi:10.1161/circulationaha.104.517011Sapp, J. L., Bar-Tal, M., Howes, A. J., Toma, J. E., El-Damaty, A., Warren, J. W., … Horáček, B. M. (2017). Real-Time Localization of Ventricular Tachycardia Origin From the 12-Lead Electrocardiogram. JACC: Clinical Electrophysiology, 3(7), 687-699. doi:10.1016/j.jacep.2017.02.024Sapp, J. L., Dawoud, F., Clements, J. C., & Horáček, B. M. (2012). Inverse Solution Mapping of Epicardial Potentials. Circulation: Arrhythmia and Electrophysiology, 5(5), 1001-1009. doi:10.1161/circep.111.970160Sapp, J. L., Wells, G. A., Parkash, R., Stevenson, W. G., Blier, L., Sarrazin, J.-F., … Tang, A. S. L. (2016). Ventricular Tachycardia Ablation versus Escalation of Antiarrhythmic Drugs. New England Journal of Medicine, 375(2), 111-121. doi:10.1056/nejmoa1513614Schulze, W. H. W., Chen, Z., Relan, J., Potyagaylo, D., Krueger, M. W., Karim, R., … Dössel, O. (2016). ECG imaging of ventricular tachycardia: evaluation against simultaneous non-contact mapping and CMR-derived grey zone. Medical & Biological Engineering & Computing, 55(6), 979-990. doi:10.1007/s11517-016-1566-xShah, D. C., Jaïs, P., Haïssaguerre, M., Chouairi, S., Takahashi, A., Hocini, M., … Clémenty, J. (1997). Three-dimensional Mapping of the Common Atrial Flutter Circuit in the Right Atrium. Circulation, 96(11), 3904-3912. doi:10.1161/01.cir.96.11.3904Shome, S., & Macleod, R. (s. f.). Simultaneous High-Resolution Electrical Imaging of Endocardial, Epicardial and Torso-Tank Surfaces Under Varying Cardiac Metabolic Load and Coronary Flow. Lecture Notes in Computer Science, 320-329. doi:10.1007/978-3-540-72907-5_33SIMMS, H. D., & GESELOWITZ, D. B. (1995). Computation of Heart Surface Potentials Using the Surface Source Model. Journal of Cardiovascular Electrophysiology, 6(7), 522-531. doi:10.1111/j.1540-8167.1995.tb00425.xSvehlikova, J., Teplan, M., & Tysler, M. (2018). Geometrical constraint of sources in noninvasive localization of premature ventricular contractions. Journal of Electrocardiology, 51(3), 370-377. doi:10.1016/j.jelectrocard.2018.02.013Tsyganov, A., Wissner, E., Metzner, A., Mironovich, S., Chaykovskaya, M., Kalinin, V., … Kuck, K.-H. (2018). Mapping of ventricular arrhythmias using a novel noninvasive epicardial and endocardial electrophysiology system. Journal of Electrocardiology, 51(1), 92-98. doi:10.1016/j.jelectrocard.2017.07.018Umapathy, K., Nair, K., Masse, S., Krishnan, S., Rogers, J., Nash, M. P., & Nanthakumar, K. (2010). Phase Mapping of Cardiac Fibrillation. Circulation: Arrhythmia and Electrophysiology, 3(1), 105-114. doi:10.1161/circep.110.853804Van Dam, P. M., Oostendorp, T. F., Linnenbank, A. C., & van Oosterom, A. (2009). Non-Invasive Imaging of Cardiac Activation and Recovery. Annals of Biomedical Engineering, 37(9), 1739-1756. doi:10.1007/s10439-009-9747-5Van Oosterom, A. (2001). Genesis of the T wave as based on an equivalent surface source model. Journal of Electrocardiology, 34(4), 217-227. doi:10.1054/jelc.2001.28896Van Oosterom, A. (2002). Solidifying the solid angle. Journal of Electrocardiology, 35(4), 181-192. doi:10.1054/jelc.2002.37176Van Oosterom, A. (2004). ECGSIM: an interactive tool for stu

Generalization and Regularization for Inverse Cardiac Estimators

Electrocardiographic Imaging (ECGI) aims to estimate the intracardiac potentials noninvasively, hence allowing the clinicians to better visualize and understand many arrhythmia mechanisms. Most of the estimators of epicardial potentials use a signal model based on an estimated spatial transfer matrix together with Tikhonov regularization techniques, which works well specially in simulations, but it can give limited accuracy in some real data. Based on the quasielectrostatic potential superposition principle, we propose a simple signal model that supports the implementation of principled out-of-sample algorithms for several of the most widely used regularization criteria in ECGI problems, hence improving the generalization capabilities of several of the current estimation methods. Experiments on simple cases (cylindrical and Gaussian shapes scrutinizing fast and slow changes, respectively) and on real data (examples of torso tank measurements available from Utah University, and an animal torso and epicardium measurements available from Maastricht University, both in the EDGAR public repository) show that the superposition-based out-of-sample tuning of regularization parameters promotes stabilized estimation errors of the unknown source potentials, while slightly increasing the re-estimation error on the measured data, as natural in non-overfitted solutions. The superposition signal model can be used for designing adequate out-of-sample tuning of Tikhonov regularization techniques, and it can be taken into account when using other regularization techniques in current commercial systems and research toolboxes on ECG

Personalized noninvasive imaging of volumetric cardiac electrophysiology

Three-dimensionally distributed electrical functioning is the trigger of mechanical contraction of the heart. Disturbance of this electrical flow is known to predispose to mechanical catastrophe but, due to its amenability to certain intervention techniques, a detailed understanding of subject-specific cardiac electrophysiological conditions is of great medical interest. In current clinical practice, body surface potential recording is the standard tool for diagnosing cardiac electrical dysfunctions. However, successful treatments normally require invasive catheter mapping for a more detailed observation of these dysfunctions. In this dissertation, we take a system approach to pursue personalized noninvasive imaging of volumetric cardiac electrophysiology. Under the guidance of existing scientific knowledge of the cardiac electrophysiological system, we extract the subject specific cardiac electrical information from noninvasive body surface potential mapping and tomographic imaging data of individual subjects. In this way, a priori knowledge of system physiology leads the physiologically meaningful interpretation of personal data; at the same time, subject-specific information contained in the data identifies parameters in individual systems that differ from prior knowledge. Based on this perspective, we develop a physiological model-constrained statistical framework for the quantitative reconstruction of the electrical dynamics and inherent electrophysiological property of each individual cardiac system. To accomplish this, we first develop a coupled meshfree-BE (boundary element) modeling approach to represent existing physiological knowledge of the cardiac electrophysiological system on personalized heart-torso structures. Through a state space system approach and sequential data assimilation techniques, we then develop statistical model-data coupling algorithms for quantitative reconstruction of volumetric transmembrane potential dynamics and tissue property of 3D myocardium from body surface potential recoding of individual subjects. We also introduce a data integration component to build personalized cardiac electrophysiology by fusing tomographic image and BSP sequence of the same subject. In addition, we develop a computational reduction strategy that improves the efficiency and stability of the framework. Phantom experiments and real-data human studies are performed for validating each of the framework’s major components. These experiments demonstrate the potential of our framework in providing quantitative understanding of volumetric cardiac electrophysiology for individual subjects and in identifying latent threats in individual’s heart. This may aid in personalized diagnose, treatment planning, and fundamentally, prevention of fatal cardiac arrhythmia

Bayesian Inference with Combined Dynamic and Sparsity Models: Application in 3D Electrophysiological Imaging

Data-driven inference is widely encountered in various scientific domains to convert the observed measurements into information that cannot be directly observed about a system. Despite the quickly-developing sensor and imaging technologies, in many domains, data collection remains an expensive endeavor due to financial and physical constraints. To overcome the limits in data and to reduce the demand on expensive data collection, it is important to incorporate prior information in order to place the data-driven inference in a domain-relevant context and to improve its accuracy. Two sources of assumptions have been used successfully in many inverse problem applications. One is the temporal dynamics of the system (dynamic structure). The other is the low-dimensional structure of a system (sparsity structure). In existing work, these two structures have often been explored separately, while in most high-dimensional dynamic system they are commonly co-existing and contain complementary information. In this work, our main focus is to build a robustness inference framework to combine dynamic and sparsity constraints. The driving application in this work is a biomedical inverse problem of electrophysiological (EP) imaging, which noninvasively and quantitatively reconstruct transmural action potentials from body-surface voltage data with the goal to improve cardiac disease prevention, diagnosis, and treatment. The general framework can be extended to a variety of applications that deal with the inference of high-dimensional dynamic systems

Doctor of Philosophy

dissertationInverse Electrocardiography (ECG) aims to noninvasively estimate the electrophysiological activity of the heart from the voltages measured at the body surface, with promising clinical applications in diagnosis and therapy. The main challenge of this emerging technique lies in its mathematical foundation: an inverse source problem governed by partial differential equations (PDEs) which is severely ill-conditioned. Essential to the success of inverse ECG are computational methods that reliably achieve accurate inverse solutions while harnessing the ever-growing complexity and realism of the bioelectric simulation. This dissertation focuses on the formulation, optimization, and solution of the inverse ECG problem based on finite element methods, consisting of two research thrusts. The first thrust explores the optimal finite element discretization specifically oriented towards the inverse ECG problem. In contrast, most existing discretization strategies are designed for forward problems and may become inappropriate for the corresponding inverse problems. Based on a Fourier analysis of how discretization relates to ill-conditioning, this work proposes refinement strategies that optimize approximation accuracy o f the inverse ECG problem while mitigating its ill-conditioning. To fulfill these strategies, two refinement techniques are developed: one uses hybrid-shaped finite elements whereas the other adapts high-order finite elements. The second research thrust involves a new methodology for inverse ECG solutions called PDE-constrained optimization, an optimization framework that flexibly allows convex objectives and various physically-based constraints. This work features three contributions: (1) fulfilling optimization in the continuous space, (2) formulating rigorous finite element solutions, and (3) fulfilling subsequent numerical optimization by a primal-dual interiorpoint method tailored to the given optimization problem's specific algebraic structure. The efficacy o f this new method is shown by its application to localization o f cardiac ischemic disease, in which the method, under realistic settings, achieves promising solutions to a previously intractable inverse ECG problem involving the bidomain heart model. In summary, this dissertation advances the computational research of inverse ECG, making it evolve toward an image-based, patient-specific modality for biomedical research

A novel simplified approach to radiofrequency catheter ablation of idiopathic ventricular outflow tract premature ventricular contractions : from substrate analysis to results

Summary: Premature ventricular contractions (PVCs) are a common finding in the general population. The most common site of PVCs, in patients without structural heart disease, is the right ventricular outflow tract (RVOT) and the left ventricular outflow tract (LVOT). The prognosis associated with frequent PVCs depends on the presence of structural heart disease, so that idiopathic PVCs have been considered benign. Recently however, evidence has emerged that a small percentage of those patients may present with polymorphic ventricular tachycardia or ventricular fibrillation or evolve to left ventricular dysfunction. Catheter ablation is indicated for frequent symptomatic PVCs refractory to medical therapy or in case of patient’s preference. Currently, catheter ablation is based on activation mapping, confirmed by pace mapping match of at least 11/12 ECG leads between the paced beat and the PVC morphology. The acute success rate ranges from 78% to 100% according to the series, and to the location of the PVCs. Remote magnetic navigation presents as a good option for PVC ablation offering a high success rate with better safety profile. Intraprocedural low PVC burden occurs in up to 30% to 48% of cases, resulting in either, cancelation of the ablation procedure in up to 11% of patients, or reduction of the success rate from 85% to 56% when ablation is attempted with pace mapping only. Recently non-invasive mapping systems based on the electrocardiogram analysis (ECGI) have been developed. These systems are capable of mapping an arrhythmia with just one beat, instead of the usual point by point acquisition, being especially useful in the case of rare arrhythmias. EGGI also constitutes a valuable noninvasive tool for studying the mechanisms of arrhythmias. With this system we were able to demonstrate the presence of an electrophysiological substrate in the RVOT of patients with PVCs and apparently normal hearts. It has been accepted for many years that in patients with idiopathic PVCs from the outflow tracts, the RVOT displays normal electroanatomical mapping features and electrophysiological properties. However, we have demonstrated that there is a substrate for idiopathic PVCs in the form of low voltage areas (LVAs) that are not detected by usual image methods including cardiac magnetic resonance (CMR). We described for the first time, the association between the presence of ST-segment elevation in V1-V2 at the 2nd intercostal space (ICS) with LVAs across the RVOT and have proposed it as a non-invasive electrocardiographic marker of LVAs. We also identified the presence of abnormal potentials in intracardiac electrograms at the ablation site during diastole, after the T wave of the surface ECG that became presystolic during the PVC and were called diastolic potentials (DPs). In Chapter V we describe in detail the study that validated those findings and evaluated the feasibility and efficacy of a proposed simplified substrate approach, for catheter ablation in patients with low intraprocedural PVC burden, defined as less than 2 PVCs/min in the first 5 minutes of the procedure. It consists of fast mapping of the RVOT in sinus rhythm looking for LVAs and DPs, identifying the area, and finally performing a restricted activation map of the PVCs at that area. Briefly, it was a prospective single-arm clinical trial at two centers and three groups were studied: a) patients with low intraprocedural PVC burden that underwent ablation with the novel simplified approach method (study group); b) patients with low intraprocedural PVC burden that underwent ablation using the standard activation mapping method between 2016 and 2018 (historical group); and c) patients without PVCs, subjected to catheter ablation of supraventricular tachycardias that agreed to have a voltage map of the RVOT in sinus rhythm performed (validation group). The calculated sample size was 38 patients in each group. The exclusion criteria were as follows: known structural heart disease, history of sustained ventricular arrhythmias, inability to perform CMR, previous ablation and standard 12-Lead ECG with evidence of conduction or electrical disease or abnormal QRS morphology were excluded. Patients in the study and validation groups, had an ECG performed at the 2nd ICS and the RVOT mapped in sinus rhythm to assess the presence of ST-segment elevation, and LVAS and DPs, respectively. The results were compared between both groups. The study group and the historical group were compared regarding the efficacy of the new simplified ablation method in terms of abolishment of the PVCs and improvement of procedure speed and success rate. When available, ECGI was performed in the study group to evaluate the accuracy of the method to identify the site of origin of the PVCs. The ECGI was performed with two systems, the Amycard (EP Solutions SA, Switzerland) and the VIVO (Catheter Precision, NJ USA). The prevalence of LVAs and DPs was significantly higher in the study group in comparison with the validation group, respectively, 71% vs 11%, p<0.0001 and 87% vs 8%, p<0.0001. The ST-segment elevation was a good predictor of LVAS with a sensitivity of 87%, specificity of 96%, positive predictor value of 93% and negative predictor value of 91%. The novel simplified approach abolished the PVCs in 90% of the patients as opposed to 47% of patients in the historical group, p<0.0001. Only 74% patients underwent ablation in the historical group versus 100% in the study group. In patients that underwent ablation, the procedure time was significantly lower in the study group when comparing to the historical group, 130 (100-164) vs 183 (160-203) min, p<0.0001 and the success rate was significantly higher, 90% vs 64%, p=0.013. The recurrence rate in patients with a successful ablation after a median follow-up time of 1060 (574-1807) days, was not significantly different between both groups, Log-Rank=0.125 ECGI before ablation was performed in 17 patients in the study group. In 6 patients the ECGI was performed just with the Amycard system, in two just with the VIVO system and in 9 patients both systems were used. We found a good agreement between the ECGI and the invasive mapping, with the predicted site of origin being in the same or contiguous segment of the ablation site in 14/15 patients (93%) with the Amycard system and in 100% of patients with the VIVO system. When both systems were used simultaneously, the agreement between them was 8/9 (90%). So, in conclusion, the proposed approach partially based on substrate mapping including searching for LVAs and DPs, proved to be feasible, faster, and more efficient than the previous approach based exclusively on activation mapping. ST-segment elevation at the 2nd ICS proved to be a good predictor of LVAs. ECGI was a valuable tool to noninvasively predict the site of origin the arrhythmia